inital

table_transformer_detection/app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, TableTransformerForObjectDetection
+import torch
+from PIL import Image
+
+def inference(img):
+    pretrained_model_path = "table-transformer-detection"
+
+    image = img.convert("RGB")
+
+    image_processor = AutoImageProcessor.from_pretrained(pretrained_model_path)
+    model = TableTransformerForObjectDetection.from_pretrained(pretrained_model_path)
+
+    inputs = image_processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # convert outputs (bounding boxes and class logits) to COCO API
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
+        0
+    ]
+
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        return (
+            f"Detected {model.config.id2label[label.item()]} with confidence "
+            f"{round(score.item(), 3)} at location {box}"
+        )
+
+
+title = "object detection:table-transformer-detection"
+description = "Gradio Demo for table-transformer-detection. To use it, simply upload your image, or click one of the examples to load them."
+article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
+examples=[['example_pdf.png']]
+
+
+demo = gr.Interface(
+    fn=inference,
+    inputs=[gr.inputs.Image(type="pil")],
+    outputs=gr.outputs.Textbox(),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples)
+
+demo.launch()

table_transformer_detection/table-transformer-detection

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+Subproject commit ab363aa6ce9eba6b79b6547bfb219bdcb2de80f8